Combining Porous Se@SiO₂ Nanocomposites and dECM Enhances the Myogenic Differentiation of Adipose-Derived Stem Cells

Background: Volumetric Muscle Loss (VML) denotes the traumatic loss of skeletal muscle, a condition that can result in chronic functional impairment and even disability. While the body can naturally repair injured skeletal muscle within a limited scope, patients experiencing local and severe muscle loss due to VML surpass the compensatory capacity of the muscle itself. Currently, clinical treatments for VML are constrained and demonstrate minimal efficacy. Selenium, a recognized antioxidant, plays a crucial role in regulating cell differentiation, anti-inflammatory responses, and various other physiological functions.

Methods: We engineered a porous Se@SiO₂ nanocomposite (SeNPs) with the purpose of releasing selenium continuously and gradually. This nanocomposite was subsequently combined with a decellularized extracellular matrix (dECM) to explore their collaborative protective and stimulatory effects on the myogenic differentiation of adipose-derived mesenchymal stem cells (ADSCs). The influence of dECM and NPs on the myogenic level, reactive oxygen species (ROS) production, and mitochondrial respiratory chain (MRC) activity of ADSCs was evaluated using Western Blot, ELISA, and Immunofluorescence assay.

Results: Our findings demonstrate that the concurrent application of SeNPs and dECM effectively mitigates the apoptosis and intracellular ROS levels in ADSCs. Furthermore, the combination of dECM with SeNPs significantly upregulated the expression of key myogenic markers, including MYOD, MYOG, Desmin, and myosin heavy chain in ADSCs. Notably, this combination also led to an increase in both the number of mitochondria and the respiratory chain activity in ADSCs.

Conclusion: The concurrent application of SeNPs and dECM effectively diminishes ROS production, boosts mitochondrial function, and stimulates the myogenic differentiation of ADSCs. This study lays the groundwork for future treatments of VML utilizing the combination of SeNPs and dECM.